In this paper, we describe our Brand Association Map (BAM) tool which maps and visualizes the way consumers naturally think and talk about brands across billions of unaided conversations online. BAM is a semi-supervised tool that leverages text-mining algorithms to identify key correlated phrases, terms and issues out of millions of candidate terms which were derived from billions of online conversations. The most correlated phrases with a given brand are then projected and plotted onto visual bull's eye representation. BAM's visualization illustrates both the correlation level between a brand (appears in the center of the visualization) and each of the highly correlated terms as well as the inner correlations among all presented terms, where terms on the same radial angel represent a "clustered" discussion of terms frequently mentioned together. We found BAM useful for extracting various intuitions and beliefs that are highly correlated with brands to better grasp how consumers really contextualize them, out of massive consumer generated media (CGM) documents.